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. Author manuscript; available in PMC: 2022 Jun 23.
Published in final edited form as: Res Nurs Health. 2020 Jul 30;44(1):37–46. doi: 10.1002/nur.22059

A Technology-based Information and Coaching/Support Program and Self-Efficacy of Asian American Breast Cancer Survivors

Eun-Ok Im 1, Jee-Seon Yi 2, Hyeoneui Kim 3, Wonshik Chee 4
PMCID: PMC9221258  NIHMSID: NIHMS1815139  PMID: 32729970

Abstract

Women’s self-efficacy for coping with breast cancer is one of the key factors that lead to successful breast cancer survivorship. Due to cultural stigma linked to breast cancer (e.g., breast cancer is a genetic disease), Asian Americans are known as a high-risk group within breast cancer survivors. However, health care providers are challenged to promote the women’s self-efficacy while considering their cultural beliefs and attitudes. In this study, the efficacy of a technology-based information and coaching/support program was examined in improving self-efficacy for coping with breast cancer among Asian American survivors. A randomized repeated measures control group study was conducted with 67 Asian American breast cancer survivors. The questions on background characteristics, the Personal Resource Questionnaire, the Perceived Isolation Scale, the Supportive Care Needs Survey Short Form 34, and the Cancer Behavior Inventory were used. The data were analyzed using repeated measurement analyses, chi-square tests, and decision tree analyses. There were significant increases in the self-efficacy scores of both control and intervention groups over time (p=.017). However, the increase in the control group’s self-efficacy scores was only up to post 1 month, and there was a decrease in the scores by post 3 months. When the participants were divided into high and low change groups based on the changes in their self-efficacy scores for 3 months, the intervention group had more participants who belonged to the high change group (p=.036). The technology-based intervention was effective in improving self-efficacy for coping with breast cancer among Asian American breast cancer survivors.

Keywords: Technology, Self-Efficacy, Asian Americans, Breast Cancer, Survivors

Introduction

Asian American breast cancer survivors reportedly suffer from unnecessary burden of symptoms and pain during their survivorship process mainly because they delay seeking help and rarely ask for information and support (Ashing-Giwa et al., 2004; Im, 2008; Im et al., 2008; Wen, Fang, & Ma, 2014). Especially their cultural attitudes toward breast cancer (e.g., cultural stigma linked to breast cancer) are major barriers in maintaining successful breast cancer survivorship (American Cancer Society, 2020; Chlebowski et al., 2005; Lim & Yi, 2009; Yi, Swartz, & Reyes-Gibby, 2011). For instance, Asian American breast cancer survivors were hesitant in disclosing their disease to others because they were afraid that their disease would be a barrier in their children’s future marriages (e.g., no family would want to marry to a family with a genetic disease)(Im et al., 2008). Subsequently, this cultural stigma frequently made Asian American breast cancer survivors delay seeking help and not to manage their survivorship concerns adequately in a timely manner.

To enhance the survivorship experience of Asian American breast cancer survivors, it is important to increase the women’s self-efficacy for coping with breast cancer while tackling their cultural hesitance by providing adequate information and coaching/support (Ashing-Giwa et al., 2004; Im, 2008; Im et al., 2008; Wen, Fang, & Ma, 2014). In this paper, based on the Bandura’s Behavioral Change theory (Bandura, 1977), self-efficacy refers to a survivor’s confidence in being able to engage successfully in coping with breast cancer, given a range of contexts and barriers. Despite strong support from families, Asian Americans tended to lack information and coaching/support (Tu et al., 2005), which subsequently decreased their self-efficacy for coping with cancer. Also, fewer sources of information and coaching/support were reported among Asian Americans than among Whites (Chlebowski et al., 2005; Lim & Yi, 2009; Yi et al., 2011), which could also reduce their self-efficacy for coping with breast cancer.

In reality, however, it is difficult for health care providers to provide cancer survivors with adequate information and coaching/support due to their busy clinical schedules (Knobf, 2002; Roundtree et al., 2011). A lack of coordination in care has also been reported as a major hindrance in meeting the women’s health care needs (Roundtree et al., 2011). Considering limitations in time and cost in clinical settings, it is practically impossible for health care providers to promote these women’s self-efficacy for coping with breast cancer while considering their cultural beliefs and attitudes (Ashing-Giwa et al., 2004; Im, 2008; Im et al., 2008; Wen, Fang, & Ma, 2014).

Technology-based programs could fill this gap in the care. Despite a lack of literature related to technology-based programs, they are known to be effective in providing information and coaching/support through providing easy access (e.g., 24 hour access, etc.; Al-Zadjali et al., 2010; Col, 2007; Saver et al., 2007). Also, non-face-to-face interactions through technology-based interventions reportedly work better for socially marginalized groups with stigmatized conditions due to anonymity (Massoudi et al., 2010; Mead et al., 2003; Moore et al., 2009; Wanner et al., 2009).

Especially, social media sites (e.g., Facebook) are popular media for communication of health messages among users in order to increase the users’ self-efficacy in coping with their health conditions (Abramson et al., 2015). Here, social media are defined as Web-based services that allow individuals to generate a public profile, develop a list of users with whom to share connections, and interact with these connections through internet interactions and instant messaging (Boyd and Ellison, 2007). Despite a small number of studies on social media, the few studies began to report the effectiveness of social media functions in providing information and coaching/support to breast cancer survivors (Abramson et al., 2015; Bender et al., 2013; Hale et al., 2014).

In this study, the efficacy of a technology-based information and coaching/support program including social media functions was examined in improving self-efficacy for coping with breast cancer among Asian American survivors. First, differences between the intervention and control groups were identified in the changes of self-efficacy scores from a pre-test to post 3-months. Then, the characteristics of the group whose self-efficacy could be effectively improved by the program were explored.

Methods

In this study, a randomized repeated measures control group design was adopted. This was a part of a larger ongoing study that examines the efficacy of a culturally tailored technology-based Information and Coaching/support program on survivorship experience of Asian American breast cancer survivors (Im et al., 2017; Im et al., 2018; Im et al., 2019; Im et al., 2020). The study was approved by the Institutional Review Board of the institution that the authors belonged.

Samples and Settings

Participants were recruited by announcing the study in both online and offline communities/groups for Asian Americans, which included primary care clinics, churches, community centers/organizations, professional groups, social media sites, and online forum groups. Informal and formal leaders (e.g., website owners, pastors, etc.) of the communities/groups were contacted first, and study announcements were made in various communities/groups by online announcements on their websites, hard copies of fliers, email blasts, etc. The study announcements were made through 129 online and offline communities/groups for Asian Americans. The study announcements included the information on the purpose and process of the study, how to participate in the study, the inclusion criteria, and the website address of the study. When potential participants came to the project website after seeing the study announcements, they were asked to review the electronic informed consent and agreed to participate by clicking a button of “I agree to participate.” Then, they were asked several questions to check if they met the inclusion and quota requirements through the Internet. When they met the criteria, they were automatically randomized into the two groups by the computer server-side program.

To be included in the study, the woman should be a self-identified Chinese, Korean, or Japanese who was older than 18 years with a diagnosed breast cancer (all stages of breast cancer diagnosed within 5 years); literate in English, Mandarin Chinese, Korean, or Japanese; and able to use a computer, tablet, or mobile device to have Internet access. The pre-test surveys were completed by 91 Asian American breast cancer survivors. Yet, only 67 (intervention group=34, control group=33) were included because 24 had missing data on self-efficacy scores. Nobody withdrew from the study, but the participants were allowed to skip the questions that they did not want to answer. Although this is a part of an ongoing larger study, this study itself is a pilot in nature due to its exploratory goal with preliminary data. For this purpose, 24 and 24–40 are adequate to get an optimal sample size for a main trial (Julious, 2005). Thus, the sample size of 67 (intervention group=34, control group=33) was adequate for the analyses that were conducted in this study.

The Technology-Based Program

The program was used to change the women’s attitudes, self-efficacy, perceived barriers, and social influences related to breast cancer survivorship by providing evidence-based information and culturally tailored coaching/support. Using the Bandura’s Behavioral Change theory (Bandura, 1977), the program was designed to improve three major concepts of the theory (self-efficacy, attitudes, and social influences), which was the reason that perceived social support (social influences), perceived isolation (social influences), and support care needs (attitudes) were considered in the data analysis process. Information (Kahn et al., 2002) and coaching/support (Kissane et al., 2004) reportedly change people’s behaviors related to health by changing their attitudes, self-efficacy, perceived barriers, and social influences.

The technology-based program included three components (social media sites, educational modules, and online resources) in five languages including English, simplified and traditional Chinese, Korean, and Japanese. These components were for both group (three sub-ethnic specific online forums) and individual coaching/support (chatting functions). There exist additional benefits of including both group and individual coaching/support (Losch et al., 2016). Group coaching/support works well for this specific cultural group due to their collectivistic culture that emphasizes the unity of community (Spector, 2012). Individual coaching/support also works well for this group because of their cultural stigma attached to breast cancer (Im, 2011; Im et al., 2008).

The three components of the intervention were culturally tailored to Asian American women using both surface and deep cultural tailoring. Surface cultural tailoring was done using culturally matched interventionists (e.g., Japanese interventionists for Japanese women), culturally tailored content (e.g., Japanese online resources for Japanese women), and multiple languages (four languages that were mainly used among the participants). Deep cultural tailoring was done using cultural examples among specific sub-ethnic groups of Asian American breast cancer survivors from previous studies (Im, 2011; Im et al., 2008). For instance, Chinese breast cancer survivors stigmatized breast cancer because they perceived it as a family inherited genetic disease that could go down to their descendants (Im, 2011; Im et al., 2008). Thus, in individual coaching/support, the interventionist tried to use the example to open the discussion on Chinese participants’ survivorship experience so that the participants could understand that they were not the only ones who had the concerns and change their cultural stigma by getting the most updated information related to breast cancer from scientific authorities. Again, all the intervention components were accessed through computers, mobile devices, and tablets. More detailed information on the intervention is available elsewhere (Im et al., 2017; Im et al., 2018; Im et al., 2019; Im et al., 2020).

Instruments

Background characteristics.

A dozen of questions were asked to assess background characteristics of the participants including their socio-demographic characteristics and health status (e.g., age, sub-ethnicity, education, religion, marital status, employed/unemployed, family income, health care access, cancer type, cancer stage, etc.).

Perceived social support.

The Personal Resource Questionnaire (PRQ-2000) assessed the women’s perceived level of social support using 15 questions on a 7-point Likert scale. The questions of the PRQ-2000 could be categorized into five dimensions: “provision for attachment/intimacy,” “social integration,” “opportunity for nurturing behavior,” “reassurance of worth,” and “the availability of informational, emotional, and material help.” Higher scores mean higher perceived social support. The construct validity of the PRQ-2000 was supported by a significant correlation with a comparable mental health measure (the Center for Epidemiological Studies Depression scale) in a previous study (Weinert, 2003). Cronbach α for the PRQ-2000 in this study was .94.

Perceived isolation.

The Perceived Isolation Scale (PIS) assessed the women’s perceived interactions or social isolation using six questions (on a 3-point Likert scale) on social support and three questions on interactions/loneliness. Higher scores mean higher social isolation. Construct validity was supported using a confirmatory factor analysis in a previous study (Cornwell & Waite, 2009). In this study, Cronbach’s α of the PIS was .86.

Support care needs.

The Supportive Care Needs Survey Short Form 34 (SCNS-SF34; on a 5-point Likert scale [1 = “not applicable” to 5 = “high needs”]) was used to measure the supportive care needs. It assesses the needs for help during the past month, including the needs for physical and daily living, the needs for psychological help, the needs for patient care and support, the needs for health system, the needs for information, and the needs related to sexuality. Higher scores mean greater needs for help. Cronbach’s α of the SCNS-SF34 was .90 in this study.

Self-Efficacy.

The women’s self-efficacy related to breast cancer survivorship was assessed using the Cancer Behavior Inventory (CBI). The CBI was specifically designed to measure self-efficacy for coping with cancer. The CBI assessed the women’s self-efficacy by rating the women’s confidence about management of stress and changes due to cancer and subsequent treatment modalities. The CBI included 14 questions (on a 9-point Likert scale; from 1 = “not at all confident” to 9 = “totally confident”). Higher scores mean greater self-efficacy. The validity of the CBI was supported through a principal factor analysis with the Varimax rotation (Merluzzi et al., 2001). Cronbach’s α of the CBI in this study was .93.

Data Collection Procedures

When a potential participant made her first visit at the project website, she was asked to select her language of choice (English, Mandarin Chinese [traditional and simplified], Korean, or Japanese). Then, she was asked to review the electronic informed consent sheet. When she gave her consent by pushing “I agree to participate,” she was automatically checked against the inclusion criteria and quota requirements using the computer server-side program. In short, the online questionnaire (that was linked to the electronic informed consent sheet) included screening questions that potential participants needed to answer. Depending on their answers to the screening questions, they were automatically excluded or included by the computer server-side program. Then, only those who met the inclusion criteria and quota requirements were allowed to enter the pre-test questionnaire by the computer server-side program. While filling out the pre-test questionnaire on the Internet, the participants were allowed to skip the questions that they did not want to answer. When the pre-test questionnaire was completed, they were randomized into intervention and control groups through an automatic randomization function of the project website. They were given their user IDs and passwords for the program and allowed to make changes in their IDs and passwords as needed.

Both groups were required to use the American Cancer Society (ACS) website related to breast cancer survivorship. The ACS website provides the information in multiple languages including Chinese and Korean. Only the intervention group was asked to use the technology-based program. During the intervention process, culturally-matched interventionists were assigned to the intervention group and provided group or individual coaching every week. Both groups were requested to fill out the second and third questionnaires at post 1-month and post 3-months. About 30 minutes were needed to complete the questionnaire each time. Both groups were asked to continue their usual information searches. Both groups received biweekly reminders and thank you emails. When the participants had any questions/concerns about data collection or intervention materials, they could contact the research team by emails or phone calls.

Data Analysis

The data were analyzed using SPSS 25.0. The alpha level was set at 0.05. First, using descriptive and inferential statistics including chi-square tests and t-tests, characteristics of the intervention and control groups were compared. Missing fields were kept as missing without substitution. The program efficacy in increasing self-efficacy was examined through repeated measurement analyses. Then, chi-square tests and decision tree analyses (algorithm = classification and regression trees [CART]) were conducted for cross-group comparisons of changes in the participants’ self-efficiency for three months. Decision tree analyses are commonly used to determine various characteristics related to targeted outcomes and to assist the decision-making process (e.g., management strategies) related to the outcomes (Kamiński, Jakubczyk, & Szufel, 2018). Thus, to identify the characteristics of the group whose self-efficacy could be effectively improved by the program, a decision tree analysis was conducted in this study using CRT at SPSS software. In the decision tree analysis, the minimum number of cases was set by 2% for the parent node and 1% for the child node in accordance with the relative criteria. The maximum tree depth of the model was 5. To evaluate the validation of the decision tree model, a 10-fold cross-validation was conducted; the accuracy of classification was 94.0%.

Result

Characteristics of the Participants

The characteristics for the participants can be found in Table 1. The retention rates in the intervention group were: 79.6% at post 1-month and 69.4% at post 3-months. The retention rates in the control group were: 90.5% at post 1-month and 78.6% at post 3-months. The average age was 52.5 years (SD=12.00), and 56.7% of the participants were Chinese. About 59.7% had a religion, and 40.3% were college graduates. About 60% considered their income was not enough for necessities (e.g., foods, housing, etc.), and 70.1% preferred using a language other than English. About 64.8% were not employed. About 85.1% were not born in the U.S., and the average years in the U.S. was 13.42 years (SD=10.47). The average years since diagnosis was 2.32 years (SD=1.47). Over 75.8% had an invasive type of breast cancer, and over 80% were diagnosed with stage I or II breast cancer. About 82.1% used medication, 55.7% did not manage their pain, and 57.4% managed their symptoms. The mean perceived social support score was 5.22 (SD=1.07), and the average perceived isolation score was 1.70 (SD=0.53). The average scores of supportive care needs were as follows; 39.62±23.88 for the physical and daily living needs, 45.26±25.75 for the psychological needs, 38.28±25.15 for the patient care and support needs, 43.58±26.98 for the health system and information needs, and 47.38±32.92 for the sexuality needs. The average self-efficacy score was 78.73 (SD=22.81). There were no significant differences in the characteristics between the control and intervention groups, which supported a successful randomization.

Table 1.

Characteristics of the participants by group at the pre-test.

Total
(n=67)		 Control
(n=33)		 Intervention
(n=34)		 t or X2 p
M±SD or N (%) M±SD or N (%) M±SD or N (%)
Age 52.500±12.000 52.037±13.060 52.903±11.199 −0.272 .787
Sub-ethnicity
 Chinese 38 (56.7) 21 (55.3) 17 (44.7) 1.292 .524
 Korean 15 (22.4) 6 (40.0) 9 (60.0)
 Japanese 14 (20.9) 6 (42.9) 8 (57.1)
Religion
 No 27 (40.3) 14 (51.9) 13 (48.1) 0.122 .806
 Yes 40 (59.7) 19 (47.5) 21(52.5)
Education level
 Middle school graduate 4 (6.0) 2 (50.0) 2 (50.0) 6.482 .166
 High school graduate 16 (23.9) 5 (31.3) 11(68.8)
 Partial college 7 (10.4) 4 (57.1) 3 (42.9)
 College graduate 27 (40.3) 12 (44.4) 15(55.6)
 Graduate degree 13 (19.4) 10 (76.9) 3 (23.1)
Income sufficiency
 No 39 (60.0) 19 (48.7) 20 (51.3) 0.010 .919
 Yes 26 (40.0) 13 (50.0) 13 (50.0)
Preferred language
 English 9 (13.4) 4 (44.4) 5 (55.6) 0.208 .901
 Equally English and another 11 (16.4) 6 (54.5) 5 (45.5)
 Another 47 (70.1) 23 (48.9) 24 (51.1)
Employment
 No 35(64.8) 16 (45.7) 19 (54.3) 0.731 .569
 Yes 19 (35.2) 11 (57.9) 8 (42.1)
US-born
 No 57 (85.1) 30 (52.6) 27 (47.4) 1.743 .305
 Yes 10 (14.9) 3 (30.0) 7 (70.0)
Length of US residence 13.416±10.475 14.940±11.204 11.939±9.659 1.158 .251
Time since diagnosis (yrs) 2.322±1.467 2.233±1.633 2.413±1.296 −0.469 .641
Type of breast cancer
 Invasive 47 (75.8) 21 (44.7) 26 (55.3) 0.018 .893
 Noninvasive (in situ) 15 (24.2) 7 (46.7) 8 (53.3)
Stage of breast cancer
 I 25 (41.0) 13 (52.0) 12 (48.0) 2.398 .494
 II 26 (42.6) 13 (50.0) 13 (50.0)
 III 8 (13.1) 3 (37.5) 5 (62.5)
 IV 2 (3.3) 0 (0.0) 2 (100.0)
Use of medicine
 No 12 (17.9) 5 (41.7) 7 (58.3) 0.337 .752
 Yes 55 (82.1) 28 (50.9) 27 (49.1)
Cancer pain management
 No 34 (55.7) 17 (50.0) 17 (50.0) 1.025 .437
 Yes 27 (44.3) 10 (37.0) 17 (63.0)
Symptom management
 No 26 (42.6) 12 (46.2) 14 (53.8) 0.001 .973
 Yes 35 (57.4) 16 (45.7) 19 (54.3)
Perceived social support 5.221±1.071 5.210±0.932 5.233±1.205 −0.088 .930
Perceived isolation 1.703±0.534 1.629±0.470 1.774±0.588 −1.111 .270
Supportive care needs
 Physical and daily living 39.626±23.889 43.333±18.228 36.029±28.144 1.264 .211
 Psychological 45.261±25.754 48.106±25.716 42.500±25.871 0.889 .377
 Patient care and support 38.283±25.159 39.697±24.746 36.911±25.849 0.450 .654
 Health system and information 43.588±26.986 44.972±28.718 42.246±25.554 0.411 .683
 Sexuality 47.388±32.927 48.232±34.217 46.568±32.119 0.205 .838
Self-efficacy 78.731±22.805 76.272±19.592 81.117±25.612 −0.868 .389
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Changes in the Self-efficacy Scores

There were significance increases in the self-efficacy scores of both control and intervention groups over time (p=.017; see Table 2). There were significant increases in the self-efficacy scores of the intervention group from the pre-test (81.12±25.61) to post 1 month (86.82±19.59) and post 3 months (86.82±19.60). However, the increase in the control group’s self-efficacy scores was only up to post 1 month (80.697±21.486), and there was a decrease in the scores by post 3 months (79.848±20.048; see Figure 1).

Table 2.

Changes in the self-efficacy scores by group and time.

Total
(n=67)		 Control
(n=33)		 Intervention
(n=34)		 p
M±SD or N (%) M±SD or N (%) M±SD or N (%) time group time *group
Self-efficacy
 Pre 78.731±22.805 76.272±19.592 81.117±25.612 .017 .130 .469
 Post 1 month 83.806±20.623 80.697±21.486 86.823±19.595
 Post 3 months 84.820±20.180 79.848±20.048 89.647±19.392
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Figure 1.

Figure 1.

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Changes in the self-efficacy scores by group and time.

Characteristics of Those with High Changes in the Self-Efficacy Scores

When the participants were categorized into two groups (one group with high changes in self-efficacy scores between the pre-test and post-3 months and the other group with low changes in self-efficacy scores) based on the average changed self-efficacy scores during 3 months (the cut point=6.089), the intervention group had more participants who belonged to the group with high changes (p=.036) (Table 3).

Table 3.

A comparison of the changes in the self-efficacy scores and the participants in the low and high change groups between the intervention and control groups.

Total
(n=67)		 Control
(n=33)		 Intervention
(n=34)		 t or X2 p
The changes in the self-efficacy scores, M±SD (post-3 months-pre-test) 6.089±21.005 3.575±18.952 8.529±22.841 −0.965 .338
Those in the low change group, N (%) 36 (53.7) 22 (61.1) 14 (38.9) 4.377 .036
Those in the high change group, N (%) 31 (46.3) 11 (35.5) 20 (42.5)
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The analysis using the decision tree method divided the total participants by their patient care and support needs scores (over 7.5 points) at the first stage. Then, in the second stage, those with over 7.5 points of the patient and support needs scores were divided into the intervention group and the control group. Then, the intervention group was divided into low and high education groups (≤ high school graduates and > high school graduates). The low educational group was divided into those with low social support scores (under 4.47) and those with high social support scores (over 4.47). The high educational group was divided by age (≤ 40.5 years old and > 40.5 years old). Then, those who were over 40.5 years old were divided into those with low social support (≤ 6.5) and those with high social support (> 6.5). The analysis indicated that those in the intervention group with over 7.5 points of the patients care and support needs scores who were in the low education group with low perceived social support had 100% impact of the intervention (see Figure 2). The analysis also indicated that those in the intervention group with over 7.5 points of the patients care and support needs scores who were in the high education group, aged over 40.5 years old, and having low perceived social support (under 6.5) had 100% impact of the intervention (Figure 2). In other words, the intervention worked better for: (a) those with high needs for patient care and support who were in the low education group with low perceived social support; and (b) those with high needs for patient care and support who were in the high education group, aged over 40.5 years, and with low perceived social support.

Figure 2.

Figure 2.

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A decision tree on the characteristics of those with higher changes in the self-efficacy scores.

Discussion

This study supported the efficacy of the technology-based program on increasing self-efficacy among Asian American breast cancer survivors. The findings are basically consistent with the literature that have reported the efficacy of technology-based interventions in making changes in health behaviors and improving health outcomes (Al-Zadjali et al., 2010; Col, 2007). Because computer and mobile technologies allow the users to have easy access to the intervention without any geographical and/or time constraints, technology-based interventions could work better for those with stigmatized health/disease conditions and marginalized populations (Pekmezi et al., 2010; Yoo et al., 2003).

The reason that the self-efficacy scores increased in both intervention and control groups in the first month could be interpreted as the effect of the attention control condition (the ACS website) or the well-known Hawthorne effect (Grove, Burns, & Gray, 2012). Maybe, a simple use of a website with information could improve Asian American breast cancer survivors’ self-efficacy for coping with cancer. Or, Asian Americans’ tendency to comply with the study requirements could have influenced the control group to report higher self-efficacy in the first month. Indeed, Asian culture is well-known about its emphasis on harmony in interactions (Spector, 2012), which could frequently cause social desirability. Although both groups had positive changes in the first month, only the intervention group had positive changes at post-3 months. This finding supports the efficacy of technology-based programs in changing health behaviors including self-efficacy for coping with breast cancer. Also, this finding agrees with the literature supporting the needs for support among Asian American breast cancer survivors (Chlebowski et al., 2005; Yi et al., 2011).

The findings from the decision tree analysis could provide guidance in decision making related to target groups for future interventions. One target group would be: those who are in the low education group (≤ high school graduates) with high needs for patient care and support and with low perceived social support. Another target group would be: those who are in the high education group (over high school graduates) with high needs for patients care and support, who are aged over 40.5 years old, and who have low perceived social support. The decision tree analysis shows the process and importance through which multiples factors influence the target variable by generating inference rules that link parent nodes to child nodes (Kamiński et al., 2018). The CART (classification and regression tree) method that was used in this study helps explain the effects of interactions among two or more variables on the target variable by determining the complicated interactions among the variables in the final tree (Zimmerman et al., 2016). Subsequently, the decision tree analysis method helps identify the most effective and eligible groups for future interventions while considering the influences of multiple factors on the target variable.

This study has some limitations. First, because this was a part of a larger ongoing study, the sample selection process could not be controlled specifically for this study. Second, the study might have potential selection bias because one of the inclusion criteria was the participants’ access to the Internet using computers or mobile devices. Third, dose-response relationships could not be examined because of the small sample size with three data collection points, multiple co-variates, and multiple intervention components to consider. Fourth, the data were collected only based on self-reports. Fifth, there might exist some bias from using culturally matched interventionists for different sub-ethnic groups (e.g., contamination by the interventionists) although training, re-training, and monitoring of the interventionists were regularly done to ensure the fidelity of the intervention. Sixth, this was a part of a larger ongoing study that did not collect any qualitative findings related to the usability or acceptability of the program. Finally, clinical meanings of the study findings could not be determined due to lack of studies using technology-based interventions among cancer survivors in general and among Asian American breast cancer survivors in specific.

Conclusions

This study supported the efficacy of a technology-based program in improving self-efficacy for coping with breast cancer among Asian American breast cancer survivors. Based on the findings, implications for future research and practice are proposed as follows. First, as the findings supported, the technology-based program was highly effective in increasing self-efficacy among two specific groups within Asian American breast cancer survivors. In clinical practice, health care providers working with Asian American breast cancer survivors could consider these two specific groups at high risk in their future development and implementation of technology-based interventions. Second, future research needs to be conducted among a larger number of Asian American breast cancer survivors in order to confirm the findings since this study included a small number of participants. Finally, future research needs to be conducted while controlling possible selection bias and while using objective measurements such as biomarkers.

Acknowledgements:

We greatly appreciate all the efforts made by the TICAA research team members and research participants.

Source of Funding:

This study was funded by the National Institutes of Health (NCI/NINR; 1R01CA203719).

Footnotes

Conflict of Interest: The authors have no conflicts of interests to report.

Contributor Information

Eun-Ok Im, Emory University.

Jee-Seon Yi, Duke University.

Hyeoneui Kim, Duke University.

Wonshik Chee, Emory University.

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